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https://github.com/ofajardo/pyreadr

Python package to read and write R RData and Rds files into/from pandas dataframes. No R or other external dependencies required.
https://github.com/ofajardo/pyreadr

pandas-dataframe python r rdata rds rds-files

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Python package to read and write R RData and Rds files into/from pandas dataframes. No R or other external dependencies required.

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README 

        
# pyreadr



A python package to read and write R RData and Rds files into/from 

pandas dataframes. It does not need to have R or other external

dependencies installed.


 



**It can read mainly R data frames and tibbles. Also supports vectors, matrices, arrays and tables.

R lists and R S4 objects (such as those from Bioconductor) are not supported. Please read the

Known limitations section and the section on what objects can be read for more information.**





This package is based on the [librdata](https://github.com/WizardMac/librdata) C library by 

[Evan Miller](https://www.evanmiller.org/) and a modified version of the cython wrapper around 

librdata

[jamovi-readstat](https://github.com/jamovi/jamovi-readstat)

by the [Jamovi](https://www.jamovi.org/) team.



Detailed documentation on all available methods is in the 

[Module documentation](https://ofajardo.github.io/pyreadr/)



If you would like to read SPSS, SAS or STATA files into python in an easy way,

take a look to [pyreadstat](https://github.com/Roche/pyreadstat), a wrapper

around the C library [ReadStat](https://github.com/WizardMac/ReadStat).



If you would like to effortlessly produce beautiful summary tables from pandas 

dataframes, take a look to [pysummaries](https://github.com/Genentech/pysummaries)



## Table of Contents



- [Dependencies](#dependencies)

- [Installation](#installation)

  * [Using pip](#using-pip)

  * [Using conda](#using-conda)

  * [From the latest sources](#from-the-latest-sources)

- [Usage](#usage)

  * [Basic Usage: reading files](#basic-usage--reading-files)

  * [Basic Usage: writing files](#basic-usage--writing-files)

  * [Reading files from internet](#reading-files-from-internet)

  * [Reading selected objects](#reading-selected-objects)

  * [List objects and column names](#list-objects-and-column-names)

  * [Reading timestamps and timezones](#reading-timestamps-and-timezones)

  * [What objects can be read](#what-objects-can-be-read-and-written)

  * [More on writing files](#more-on-writing-files)

- [Known limitations](#known-limitations)

- [Contributing](#contributing)

- [Change Log](#change-log)

- [People](#people)



## Dependencies



The package depends on pandas, which you normally have installed if you got Anaconda (highly recommended.) If creating

a new conda or virtual environment or if you don't have it in your base installation, pandas should get installed automatically.



If you are reading 3D arrays, you will need to install xarray manually. This is not installed automatically as most users

won't need it.



In order to compile from source, you will need a C compiler (see installation) and cython 

(version >= 0.28).



librdata also depends on zlib, bzip2 and lzma; it was reported not to be installed on Lubuntu or docker base ubuntu

images. If you face this problem intalling the libraries solves it.



## Installation



### Using pip



Probably the easiest way: from your conda, virtualenv or just base installation do:



```

pip install pyreadr

```



If you are running on a machine without admin rights, and you want to install against your base installation you can do:



```

pip install pyreadr --user

```



We offer pre-compiled wheels for Windows,

linux and macOs.



### Using conda



The package is also available in [conda-forge](https://anaconda.org/conda-forge/pyreadr) 

for windows, mac and linux 64 bit.



In order to install:



```

conda install -c conda-forge pyreadr 

```



### From the latest sources



Download or clone the repo, open a command window and type:



```

python3 setup.py install

```



If you don't have admin privileges to the machine do:



```

python3 setup.py install --user

```



You can also install from the github repo directly (without cloning). Use the flag --user if necessary.



```

pip install git+https://github.com/ofajardo/pyreadr.git

```



You need a working C compiler and cython. You may also need to install bzlib (on ubuntu install libbz2-dev).



In order to run the tests:



```

python tests/test_basic.py

``` 



You can also install and test in place with:



```

python setup.py build_ext --inplace

python tests/test_basic.py --inplace

```



## Usage



### Basic Usage: reading files



Pass the path to a RData or Rds file to the function read_r. It will return a dictionary 

with object names as keys and pandas data frames as values.



For example, in order to read a RData file:



```python

import pyreadr



result = pyreadr.read_r('test_data/basic/two.RData')



# done! let's see what we got

print(result.keys()) # let's check what objects we got

df1 = result["df1"] # extract the pandas data frame for object df1

```



reading a Rds file is equally simple. Rds files have one single object, 

which you can access with the key None:



```python

import pyreadr



result = pyreadr.read_r('test_data/basic/one.Rds')



# done! let's see what we got

print(result.keys()) # let's check what objects we got: there is only None

df1 = result[None] # extract the pandas data frame for the only object available

```



Here there is a relation of all functions available. 

You can also check the [Module documentation](https://ofajardo.github.io/pyreadr/).



| Function in this package | Purpose |

| ------------------- | ----------- |

| read_r        | reads RData and Rds files |

| list_objects  | list objects and column names contained in RData or Rds file |

| download_file | download file from internet |

| write_rdata   | writes RData files |

| write_rds     | writes Rds files   |



### Basic Usage: writing files



Pyreadr allows you to write one single pandas data frame into a single R dataframe

and store it into a RData or Rds file. Other python or R object types 

are not supported. Writing more than one object is not supported.



```python

import pyreadr

import pandas as pd



# prepare a pandas dataframe

df = pd.DataFrame([["a",1],["b",2]], columns=["A", "B"])



# let's write into RData

# df_name is the name for the dataframe in R, by default dataset

pyreadr.write_rdata("test.RData", df, df_name="dataset")



# now let's write a Rds

pyreadr.write_rds("test.Rds", df)



# done!



```



now you can check the result in R:



```r

load("test.RData")

print(dataset)



dataset2 <- readRDS("test.Rds")

print(dataset2)



```



By default the resulting files will be uncompressed, you can activate gzip compression

by passing the option compress="gzip". This is useful in case you have big files.



```python

import pyreadr

import pandas as pd



# prepare a pandas dataframe

df = pd.DataFrame([["a",1],["b",2]], columns=["A", "B"])



# write a compressed RData file

pyreadr.write_rdata("test.RData", df, df_name="dataset", compress="gzip")



# write a compressed Rds file

pyreadr.write_rds("test.Rds", df, compress="gzip")



```



### Reading files from internet



Librdata, the C backend of pyreadr absolutely needs a file in disk and only a string with the path

can be passed as argument, therefore you cannot pass an url to pyreadr.read_r. 



In order to help with this limitation, pyreadr provides a funtion download_file which as its name

suggests downloads a file from an url to disk:



```python

import pyreadr



url = "https://github.com/hadley/nycflights13/blob/master/data/airlines.rda?raw=true"

dst_path = "/some/path/on/disk/airlines.rda"

dst_path_again = pyreadr.download_file(url, dst_path)

res = pyreadr.read_r(dst_path)

```



As you see download_file returns the path where the file was written, therefore you can pass it

to pyreadr.read_r directly:



```python

import pyreadr



url = "https://github.com/hadley/nycflights13/blob/master/data/airlines.rda?raw=true"

dst_path = "/some/path/on/disk/airlines.rda"

res = pyreadr.read_r(pyreadr.download_file(url, dst_path), dst_path)

```



### Reading selected objects



You can use the argument use_objects of the function read_r to specify which objects

should be read. 



```python

import pyreadr



result = pyreadr.read_r('test_data/basic/two.RData', use_objects=["df1"])



# done! let's see what we got

print(result.keys()) # let's check what objects we got, now only df1 is listed

df1 = result["df1"] # extract the pandas data frame for object df1

```



### List objects and column names



The function list_objects gives a dictionary with object names contained in the

RData or Rds file as keys and a list of column names as values.

It is not always possible to retrieve column names without reading the whole file

in those cases you would get None instead of a column name.



```python



import pyreadr



object_list = pyreadr.list_objects('test_data/basic/two.RData')



# done! let's see what we got

print(object_list) # let's check what objects we got and what columns those have



```



### Reading timestamps and timezones



R Date objects are read as datetime.date objects.



R datetime objects (POSIXct and POSIXlt) are internally stored as UTC timestamps, and may have additional timezone

information if the user set it explicitly. If no timezone information

was set by the user R uses the local timezone for display. 



librdata cannot retrieve that timezone information, therefore pyreadr display UTC time by default, which will not match the

display in R. You can set explicitly some timezone (your local timezone for example) with the argument timezone for the

function read_r



```python

import pyreadr



result = pyreadr.read_r('test_data/basic/two.RData', timezone='CET')



```



if you would like to just use your local timezone as R does, you can 

get it with tzlocal (you need to install it first with pip) and pass the 

information to read_r:



```python



import tzlocal

import pyreadr



my_timezone = tzlocal.get_localzone().zone

result = pyreadr.read_r('test_data/basic/two.RData', timezone=my_timezone)



```



If you have control over the data in R, a good option to avoid all of this is to transform

the POSIX object to character, then transform it to a datetime in python.



When writing these kind of objects pyreadr transforms them to characters. Those can be easily

transformed back to POSIX with as.POSIXct/lt (see later).



### What objects can be read and written



Data frames composed of character, numeric (double), integer, timestamp (POSIXct 

and POSIXlt), date, logical atomic vectors. Factors are also supported.



Tibbles are also supported.



Atomic vectors as described before can also be directly read and are 

translated to a pandas data frame with one column. 



Matrices, arrays and tables are also read and translated to pandas data frames

(because those objects in R can be named, and plain numpy arrays do not support

dimension names). The only exception is 3D arrays, which are translated to a

xarray DataArray (as pandas does not support more than 2 dimensions). This is also

the only time that an object different from a pandas dataframe is returned by read_r.



For 3D arrays, consider that python prints these in a different way as R does, but still

you are looking at the same array (see for example [here](https://rstudio.github.io/reticulate/articles/arrays.html#displaying-arrays) for an explanation.)



Only single pandas data frames can be written into R data frames.



Lists and S4 objects (such as those coming from Bioconductor are not supported. Please read the Known limitations section for more

information.



### More on writing files



For converting python/numpy types to R types the following rules are

followed:



| Python Type         | R Type    |

| ------------------- | --------- |

| np.int32 or lower   | integer   |

| np.int64, np.float  | numeric   |

| str                 | character |

| bool                | logical   |

| datetime, date      | character |

| category            | depends on the original dtype |

| any other object    | character |

| column all missing  | logical   |

| column with mixed types | character |



* datetime and date objects are translated to character to avoid problems

with timezones. These characters can be easily translated back to POSIXct/lt in R

using as.POSIXct/lt. The format of the datetimes/dates is prepared for this

but can be controlled with the arguments dateformat and datetimeformat 

for write_rdata and write_rds. Those arguments take python standard

formatting strings.



* Pandas categories are NOT translated to R factors. Instead the original

data type of the category is preserved and transformed according to the

rules. This is because R factors are integers and levels are always

strings, in pandas factors can be any type and leves any type as well, therefore

it is not always adecquate to coerce everything to the integer/character system.

In the other hand, pandas category level information is lost in the process.



* Any other object is transformed to a character using the str representation

of the object.



* Columns with mixed types are translated to character. This does not apply to column

cotaining np.nan, where the missing values are correctly translated.



* R integers are 32 bit. Therefore python 64 bit integer have to be 

promoted to numeric in order to fit.



* A pandas column containing only missing values is transformed to logical,

following R's behavior.



* librdata writes Numeric missing values as NaN instead of NA. In pandas we only have np.nan both as 

NaN and missing value representation, and it will always be written as NaN in R.



## Known limitations



* POSIXct and POSIXlt objects in R are stored internally as UTC timestamps and may have

in addition time zone information. librdata does not return time zone information and

thefore the display of the tiemstamps in R and in pandas may differ.



* Librdata reads arrays with a maximum of 3 dimensions. If more dimensions are present

you will get an error. Please submit an issue if this is the case. 



* **Lists are not read**.



* **S4 Objects and probably other kind of objects, including those that depend on non base R packages (Bioconductor for example) cannot be read.**

 The error code in this case is as follows:



```python

"pyreadr.custom_errors.LibrdataError: The file contains an unrecognized object"

```



* Data frames with special values like arrays, matrices and other data frames

are not supported.



* librdata first de-compresses the file in memory and then extracts the

data. That means you need more free RAM than the decompress file ocuppies

in memory. RData and Rds files are highly compressed: they can occupy

in memory easily 40 or even more times in memory as in disk. Take it into

account in case you get a "Unable to allocate memory" error (see [this](https://github.com/ofajardo/pyreadr/issues/3) )



* When writing numeric missing values are translated

to NaN instead of NA.



* Writing rownames is currently not supported.



* Writing is supported only for a single pandas data frame to a single

R data frame. Other data types are not supported. Multiple data frames

for rdata files are not supported.



* RData and Rds files produced by R are (by default) compressed. Files produced

by pyreadr are not compressed by default and therefore pretty bulky in comparison. You

can pass the option compress="gzip" to write_rds or write_rda in order to activate 

gzip compression.



* Pyreadr writing is a relative slow operation

compared to doint it in R.



* Cannot read RData or rds files in encodings other than utf-8.



Solutions to some of these limitations have been proposed in the upstream librdata [issues](https://github.com/WizardMac/librdata/issues) (points 1-4 are addressed by issue 12, point 5 by issue 16 and point 7 by issue 17). However there is no guarantee that these changes will be made and there are no timelines either. If you think it would be nice if these issues are solved, please express your support in the librdata issues.



## Contributing



Contributions are welcome! Please chech the document [CONTRIBUTING.md](CONTRIBUTING.md) for more details.



## Change Log



A log with the changes for each version can be found [here](https://github.com/ofajardo/pyreadr/blob/write_support/change_log.md)



## People



[Otto Fajardo](https://github.com/ofajardo) - author, maintainer



[Jonathon Love](https://jona.thon.love/) - contributor (original cython wrapper from jamovi-readstat and msvc compatible librdata)



[deenes](https://github.com/deeenes) -  reading lzma compression



[Daniel M. Sullivan](www.danielmsullivan.com) - added license information to setup.py